In virtually every aspect of modern day business, quality control is a critical function. For example, quality control is important for products, processes, services, systems, organizations, etc. As is well understood, quality control is measured in many different ways, depending upon the particular situation.
For the past several years, quality control has become a well adopted business management strategy or style. The Six Sigma business management strategy is a well known methodology for quality control. In essence, Six Sigma methodologies are one method or tool utilized to manage or control the quality of business processes.
One tool utilized in the Six Sigma management strategy is a control chart. Generally speaking, a Six Sigma control chart utilizes statistical rules to determine if a certain measurement or pattern of measurements deviates far enough from an average or baseline value to be considered “unexpected.” Stated differently, control chart rules are generally used to distinguish an unexpected or abnormal change from an expected fluctuation in measurements, over time. In this particular case Six Sigma refers to the use of the statistical measure of standard deviation. More specifically, Six Sigma refers to a warning or control limit being set three standard deviations above or three standard deviations below the average or baseline value.
Although the above referenced Six Sigma control chart is well accepted, various similar control charts can also provide meaningful and helpful information for business management. For example, it may be desirable to determine whether changes are one or two standard deviations above or below the designated baseline. This measurement may provide an early indication of a potential issue. Similarly, it may be necessary to change or alter the statistical data set, thus changing the perspective of the control chart. As an example, management may want to focus on one particular aspect of their operations (for example one particular facility, one product line, one manufacturing machine, etc.).
As can be anticipated, the above mentioned control charts involve considerable amounts of statistical data processing. To generate a Six Sigma control chart a database is typically queried to calculate specific variables, or a data set is manually collected for use in digital spreadsheet tools. For example, this typically includes identifying a target data set, determining an average or baseline value for the identified data set and calculating the standard deviation values. This is a very data intensive project and can involve considerable processing, thus requiring significant processing power and time.
To provide additional levels of information to relevant managers, it would be beneficial to provide a tool capable of flexibly generating, and easily modifying control charts as desired. Further, it is desirable to provide a data structure which allows for the necessary data processing to be completed in an efficient and an effective manner, so that various control charts can be more easily developed. Lastly, it is also beneficial to provide a methodology for presenting control chart information to users in a valuable and efficient way.